The present invention relates to relevancy ranking in an information retrieval system. More specifically, the present invention relates to a method and system for relevancy ranking of products in online shopping.
The Internet has enabled online shopping, which has become popular because of its ease of use and fast processing. Further, it enables users to track down the vendors of a product online, and provides them with the facility of instant comparison of prices. This has made online shopping time-efficient and convenient, compared to conventional shopping. Search algorithms on the Internet help buyers to find any product from the multitude of products available online. Hence, a buyer can easily collect information about a product of his/her choice and purchase it accordingly.
However, the information available on the Internet is unstructured and unmanageable. Search engines provide results that have been sorted out to provide those that are relevant to the users, to help them search for products while shopping online. The sorting of results is based on context-based analysis of information, link analysis, or page-ranking algorithms. The results may be sorted, for example, based on the web pages that are frequently visited.
However, online product shopping is different from typical web search. For example, a query ‘blue shirt’ using existing search techniques would yield all type of results such as merchant pages, reviews, wikipedia entries, personal webpages, music bands, etc. This means the search results may not be related to the user intent—where the user expects to see a list of merchants selling blue colored shirts online with pictures and prices. Hence, the use of the existing web or content search techniques in online shopping may not yield relevant results. Further, the existing online shopping search techniques do not rank the search results, i.e., products based on product attributes such as brand, style, trend, and the like. Ranking products belonging to a particular category, based on their attributes, enables a user to compare products and helps him/her to make the best choice. This is because the user may be interested in, for example, products of a well known brand, products sold at a particular store in his locality, products of a top selling styles, products on sale, newly introduced products and the like. Therefore, the relevance or goodness of a product belonging to a particular category needs to be determined based on these factors to achieve experiential relevance. The goodness value refers to the relevance of the product to users. In other words, the goodness value indicates how good a product is given the market characteristics. In other words, to provide the user the best choices.
In light of the above discussion, there is a need for a method for ranking the results for a query in online shopping such that it provides the best results to the user considering the market demand-supply characteristics of products in a product category.